Final Project

Group 1:

Hiba Awan
Nathania Stephens

Abstract

Introduction & Background

Motivation/ Purpose

In 2023, there were over 30,000 arrests and close to 65,000 citations in Fairfax County. The Fairfax County boundaries, include areas such as Centreville, Chantilly, Herndon, Reston, Tysons Corner, McLean, Merrifield, George Mason, Annadale, Burke, Springfield, Alexandria, Lorton to name a few. If you live, work, or study in these areas then this project should be of interest to you. This project aims to inform Fairfax County patrons of crime information and hopefully provide some statistical insights that could be applicable.

Goals/ Objectives

In order to provide relevant and insightful crime information, several different visualization methods were applied to help easily interpret and compare data. Statistical learning techniques were utilized to help understand statistic significantly factors and associations between variables. Since the data utilized for this project is largely categorical the project focuses on techniques such as Chi-Squared Test, Logistic Regression, Decision Trees and Random Forest.

Data

Overview

About the Data

Three datasets were pulled from the Fairfax County Police Department website. They covered arrest, citations, and warnings in the year 2023. For simplicity general definitions are provided:

Arrest - When a person is taken into custody to answer for an offense or when there is a deprivation or restraint of a person’s liberty in any significant way.

Citation - Formal notice issued by law enforcement officer for a violation of law, typically related to traffic laws or other minor offenses. Typically requiring a violator to appear in court or pay a fine.

Warning - When a violation, typically minor, has been made but an officer issues a warning rather than a citation.

The data sets included between 24 and 34 variables, but some of many of the variables were redundant or were not applicable to the research (e.g. web_address, phone_number, name). The following attributes were key to the research conducted:

Column Name	Data Type	Description
Date	Date	Date of Violation
Time	Chr	Time of Violation
Offense	Chr	Description of Violation
Gender	Chr	Gender of Violator
Ethnicity	Chr	Hispanic or Non-Hispanic
District	Chr	Administrative area
Latitude	Dbl	Coordinates measuring north/ south of equator
Longitude	Dbl	Coordinates measuring east/ west of prime meridian
Outcome	Chr	Result of violation, arrest, citation, or warning

Limitations and Assumptions

Due to the nature of the data available on the Fairfax County Police Department website, analysis was limited to qualitative techniques. The approach taken for the project focused on predicting through qualitative responses or classification. This means that each record pulled from the Fairfax County Police Department (FCPD) would be assigned to a category or class.

While understanding local crime is the goal of this project, the data acquired only accounts for crime that was recorded by FCPD. It does not take into account crimes that were not report or any other crime that was not reported through FCPD channels.

Cleaning and Transformation

To address questions related to gender, the data needed to be standardized and correctly categorized. Column names needed to be consistent across the three datasets to merge. Gender was used over Sex. Next the column data would be transformed to consistent labels, e.g. Male, Female, and Other/Unknown. Total proportion for Gender was examined, to verify that other/ unknown class could be removed without…

Research Questions

Is there an association between gender and warnings?
Are there other factors that determine if someone gets out of a “ticket”? OR Are you more likely to get a ticket at the end of the month (some believe that police officers have a monthly quota)

Research & Analysis

Question 1: Is there an association between gender and warnings?

To address this question the null and alternative hypothesis are established.

Null Hypothesis: There is no link between gender and violation outcome (warning or citation).

Alternative Hypothesis: There is a link between gender and violation outcome.

According to the cleaned and combined dataset for warnings and citation, there was a total of 88,320 records. By looking at the counts for each outcome (citation or warning), there are a lot more citations than there are warnings given out by FCPD. This stacked bar chart also shows that males have a higher count for both categories. i

Next, the warning rate for gender is calculated. This looks at the probability of a male or female violator receiving a Warning instead of a citation e.g. getting out of a ticket. To calculate warning rate, the number of warnings are divided by the total number of incidents. \[ \frac{\text{Number of Warnings}}{\text{Total Incidents (Warnings + Citations)}} \]

This shows a slight difference in proportion between the two genders, with females having a higher warning rate than males. Is this difference significant or is it a result of chance or other factors? To help understand these results, the Chi-Squared Test is used.

\[ \chi^2 = \sum \frac{(O-E)^2}{E} \] To implement the Chi-Square Test, a comparison of expected and observed counts are calculate.

	Expected Citations	Observed Citations	Expected Warnings	Observed Warnings
Male	42,891	43,657	16,173	15,408
Female	21,243	20,478	8,011	8,777

        BinaryOutcome
Gender       0     1
  Female 20478  8777
  Male   43657 15408


    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_tbl
X-squared = 150.62, df = 1, p-value < 2.2e-16

        BinaryOutcome
Gender          0         1
  Female 21243.99  8011.007
  Male   42891.01 16173.993

Another Questions….

To address each of these question, first exploratory analysis should be done to gain an understanding and summary of the crime metrics for Fairfax County. This includes understanding what type of crimes occurred the most and where.

General crime Mapping the arrest data for a geospatial visual of where arrest occur.

Next we look at the Top 10 Arrest Type by Incident Based Reporting (IBR) codes.

Next examining the Top 10 Citations

Warning Versus Citation Next an examination of warning versus citation will be observed… This will help understand what different factors could play into getting a warning or a citation.